Principal Emerging Technology Architect - Gen AI

At U.S. Bank, we’re on a journey to do our best. Helping the customers and businesses we serve to make better and smarter financial decisions and enabling the communities we support to grow and succeed.  We believe it takes all of us to bring our shared ambition to life, and each person is unique in their potential. A career with U.S. Bank gives you a wide, ever-growing range of opportunities to discover what makes you thrive at every stage of your career. Try new things, learn new skills and discover what you excel at—all from Day One.

Job Description

U.S. Bank is seeking a Principal Emerging Technology Solutions Architect focused on generative AI to evaluate, design, and guide adoption of LLM-based capabilities across the enterprise. This role focuses on translating emerging generative AI technologies into secure, scalable, and reusable architecture patterns that enable innovation while aligning to enterprise standards.

This role has a dedicated focus on generative AI, including LLM application architecture, retrieval-augmented generation (RAG), prompt and orchestration patterns, agentic AI considerations, and responsible enterprise adoption.

This role partners across platform engineering, developer experience, architecture, data, risk, and security teams to evaluate Gen AI capabilities, define solution patterns, and provide practical technical guidance that supports implementation and operational readiness.

Responsibilities

• Evaluate generative AI platforms, tools, and frameworks such as Azure OpenAI, AWS Bedrock, enterprise LLM platforms, GitHub Copilot, Cursor, LangChain, LangGraph, and MCP gateway solutions for enterprise adoption
• Design and recommend LLM-based solution patterns, including RAG, prompt orchestration, tool calling, agentic workflows, model selection, context management, and AI-assisted developer workflows
• Recommend architecture patterns based on cost, scalability, latency, security, compliance, model risk, data protection, observability, and operational support tradeoffs
• Define reusable Gen AI integration approaches using APIs, service-based architectures, vector search, retrieval pipelines, MCP or similar orchestration patterns, and governed access to enterprise data and tools
• Provide technical depth and architecture guidance to help downstream engineering teams move Gen AI solutions from exploration and proof-of-concept into secure, scalable implementation
• Evaluate vendors, models, frameworks, and ecosystem offerings for enterprise fit, implementation readiness, governance alignment, long-term viability, and measurable business value
• Translate complex Gen AI architecture concepts into clear recommendations for technical teams, risk partners, business stakeholders, and senior leaders
• Assess early-stage Gen AI capabilities and translate findings into enterprise-ready recommendations, reference patterns, decision criteria, and adoption guidance

Basic Qualifications

• Bachelor’s degree or equivalent work experience
• Eight (8) or more years of experience in software engineering, architecture, or related technical roles

Preferred Skills / Experience

• Strong foundation in software engineering, solution architecture, and enterprise Gen AI application design
• Experience designing distributed systems and integrating platforms through APIs, microservices, event-driven patterns, and modern service-based architectures
• Cloud experience in AWS or Azure, including familiarity with Gen AI and AI/ML services such as Azure OpenAI, Azure AI services, AWS Bedrock, SageMaker, or comparable enterprise AI platforms
• Demonstrated understanding of generative AI architecture, including LLM application patterns, RAG, prompt engineering, orchestration, tool use, model evaluation, context window management, latency, cost, and safety alignment
• Experience evaluating or designing AI-assisted developer productivity capabilities such as GitHub Copilot, Cursor, code generation, internal developer assistants, or similar Gen AI tooling
• Exposure to LLM integration patterns, RAG pipelines, vector search, embeddings, prompt orchestration, LangChain, LangGraph, MCP, agentic workflows, or comparable emerging AI frameworks
• Experience making Gen AI architecture decisions grounded in real-world tradeoffs, including cost, scalability, latency, security, compliance, data access, ri…